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Record W2795286600 · doi:10.18174/444615

Verification of PUM’s intervention logic: Insights from the PRIME toolbox

2018· report· en· W2795286600 on OpenAlexaff
Fédes van Rijn, Giel Ton, Karen Maas, Haki Pamuk, Job Harms, Just Dengerink, Yuka Waarts, Carly Relou, B.I. de Vos, Frank Hubers

Bibliographic record

Venuenot available
Typereport
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsImpact
Fundersnot available
KeywordsToolboxPrime (order theory)Computer scienceProgramming languageMathematicsCombinatorics

Abstract

fetched live from OpenAlex

PUM aims to contribute to sustainable economic development by enabling Dutch senior experts to transfer knowledge to small and medium enterprises in developing countries, thereby improving their performance.PUM Netherlands senior experts aim to contribute to sustainable economic development in developing countries by improving the performance of small and medium enterprises (SMEs).PUM wants to achieve this aim by linking Dutch senior experts to SMEs in developing countries to stimulate knowledge transfer, help SMEs to apply for grants and promote business links with Dutch companies.With increased knowledge levels, new business links and access to grants, SMEs are expected to improve their business practices.That, in turn, should lead to higher turnover and profit, better employment, increased sustainability and more business with Dutch companies.Each of these outcomes is expected to contribute to sustainable and inclusive economic growth.The PRIME Partnership between PUM, CBI, Wageningen Economic Research and the Erasmus School of Economics monitors impact of PUM and CBI support to SMEs.The PRIME partnership was established in 2013 to develop and implement a methodology to monitor and evaluate the real-time impact of private-sector development support by PUM and the Centre for the Promotion of Imports from developing countries (CBI).PRIME stands for Pioneering Real-time Impact Monitoring and Evaluation and is a research partnership between Wageningen Economic Research and the Erasmus School of Economics, supported by PUM and CBI.The PRIME partnership assesses the impact of PUM and CBI support to SMEs.PRIME has developed a data collection system and an innovative mixed methods design to verify the assumptions behind PUM's theory of change.The PRIME partnership has developed a data collection system that makes it possible to verify the assumptions behind PUM's theory of change and assist PUM in monitoring P a g e | 7PRIME data show a correlation between PUM's perceived contribution to business knowledge and the number of business practices adopted, confirming PUM's assumption that the knowledge acquired by PUM experts helps to improve SMEs' business practices.PUM's perceived contribution to better practices is quite evenly distributed in terms of business size and sector, with only small differences between sectors and business sizes. PUM's contribution to better practices has helped to improve SMEs' performance.The data show that the support PUM provides to improve business practices has translated into better business performances.The number of SMEs reporting an increase in profit is on average two to three per cent higher for those benefitting from PUM's contribution to better practices than for those who did not receive support from PUM.Typically, a company's sales increased by about €11,000 (34%) following PUM support compared to sales before PUM support.The effect of PUM's support on employment varies according to the focus of the missions.Missions that focus on efficient ways of organising the business increase employment in the business by 33%, while missions that focus on financial management reduce employment by 18%.However, missions that focus on financial management target companies that have financial problems and have to cut costs. PUM's impact on SMEs' performance differs significantly between sectors and country income group.The data show that sales and employment growth is stronger in the tourism & catering sector than in the agriculture & horticulture and food & beverage sectors.The sales for a typical SME operating in the tourism & catering sector doubles following PUM missions, while the increases in sales of SMEs in the food & beverage and agriculture & horticulture sectors are about 32% and 48% after PUM missions.Moreover, PUM missions are more successful at improving the sales performance of businesses in least-developed countries.Following PUM missions the sales of a median PUM firm from a least-developed country increases by 61% compared to the sales before PUM missions, while this increase is 25% for the median PUM firm from a lower middle-income country.The effectiveness of the support depends to a large extent on the qualities of the expert, the timing of the mission, and the communication between the expert and the SME manager.PUM's interventions are not always successful in boosting a firm's performances.During the cases studies, several firms indicated that the expert that was dispatched did not have the specific knowledge or expertise to help the firm with the particular problem it was facing.In other cases, the language barrier prevented effective communication.The lack of (advanced) Spanish-speaking experts was mentioned by both SMEs and local representatives as a major issue that impacts the effectiveness of PUM experts in Latin America. Results from this study vary significantly between different type of companies and under different conditions, pointing to important enablers of PUM's effectiveness.Some conditions seem to be more enabling for larger impacts, and some types of firm seem better suited to the support modalities used by PUM.Important enablers of PUM's effectiveness are the presence of strong business support organisations (BSOs).BSOs work to improve the business environment for public sector policy and investment programmes.According to the supported firms, another key enabler was access to finance to implement certain changes in business practices.Finally, both PUM's staff and policymakers consider more coordination with other Dutch privatesector support organisations to be important enablers of effectiveness.PRIME results confirm PUM's theory of change as they demonstrate PUM's contribution to knowledge transfer and better practices, which have increased exports, profits and employment.Summarising the results of this study, we can conclude that PUM positively influenced knowledge transfer and better business practices among firms supported by PUM.Not only do the improved knowledge levels correlate with improved business practices, but the data also show that better business practices improve business performances.Firms supported by PUM have significantly increased their sales and profits, and the missions that have focused on improving business organisation have had a positive effect on employment.PUM's support has also proven to complement existing support: the level of expertise offered by PUM experts is considered to be unavailable in the local market.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0030.011
Scholarly communication0.0060.014
Open science0.0020.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0340.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.296
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2018
Admission routes1
Has abstractyes

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